Plugins

OpenViking memory and context bundle for DeepSeek Harness: pre-step auto-recall and profile injection, session capture, `viking://` URI guarding, and recall/write memory tools backed by an OpenViking server.

Local-first memory plugin for DeepSeek Harness: layered L1/L2/L3 memory with automatic capture, manual memos_* retrieval tools, and a built-in memory viewer.

ShiGuang Memory Engine (SGME) bridge: multi-agent shared long-term memory via HTTP — L0/L1/L1.5/L2 distillation, scenario-based injection, unified search, and proactive care signals (memory_search / wiki_search / signal_pull / signal_claim / signal_ack), installable as `dsh-sgme`.

DSH-native integration layer for the Minta memory engine: composes the official dsh-mcp-client row, registers the minta-memory-governance skill plus a session-start memory prewarm, and ships a Minta agent preset; requires the separately deployed Minta engine (Python/Docker) to provide the 19 MCP tools - not usable without it.

MemWeave (dsh-mem-weave) weaves memory into persona: layered memory and a persona editor in one DeepSeek Harness plugin, with a bounded per-turn injection budget, decay and consolidation, a candidate and conflict review queue, 24-hour undo, and calendar reminders, all stored in a local SQLite file.

A judgment core for DeepSeek Harness: a cognition loop that gives every decision a source and attribution and drives planning to a close, hippocampus memory that condenses running experience into knowledge (journal ledger, episodic archive, low-confidence decay), judgment identity responsibilities in the architecture (consistent rubric, judgment bound to its source), and an A2A memory-management team via dsh-linghun-assembler. Requires DSH ^0.1.0-rc.7 (rc.2 supported).

Forgetting-curve in-place dialogue compression for DeepSeek Harness: recent turns stay verbatim, middle turns are structurally dehydrated (zero-LLM), ancient turns fold into a bounded summary pair — unbounded conversations on a bounded memory budget.

Project-scoped deterministic execution trace memory and zero-token session digest plugin for DSH, compiling session histories into guarded playbooks and behavior summaries with inline session_id lineage. Registers the /tmem slash command, recalls memories only on explicit file-path or error anchors in agent/pre-step (skipping unchanged repeated steps), and intercepts tools/post-execute errors and code edits with same-origin L2 fixes, failure-loop warnings, and statistical L1 co-change partner hints (skipping routine read calls when idle). Needs Python 3 on PATH (or TMEM_PYTHON) and spawns python3 -m tmem.cli with a 5-second timeout; stores all data inside each project's .tmem/ directory (no ~/.tmem/ global store) and makes no network requests. It needs Python 3 on PATH (or TMEM_PYTHON) and runs python3 -m tmem.cli synchronously, with a 5-second timeout, on every agent step and tool result; traces are stored in .tmem/ inside each project and in ~/.tmem/ shared across projects. It makes no network requests.

Brings Harness conversations and tasks into a Live2D character on the Windows desktop, with drawer-based memory. The desktop app includes the whale girl 哦鲸鲸 as its default character. The modular design supports optional voice components for voice conversations and custom character voices.

Cross-session long-term memory backed by a local Go service (~50MB RAM, SQLite-vec + verify-augmented claims): zero-lag recall injection on every user message, session-end transcript ingest, and memory_search / memory_logs / memory_ingest tools. Install with `dsh plugin --profile web add @djasdh/interest-memory-dsh-bridge`. Protocol bridge only: the Go service is not installed by `dsh plugin add` — install and start it per the repository README first; until it runs, the tools silently return nothing.

Codex-style per-session memory draft for DeepSeek Harness: one session one draft (rollout_summaries), restrained & passive (write only on explicit user request), layered disclosure (summary + registry), idempotent integration watermark. / 一会话一草稿记忆插件。

Long-term memory and self-evolving skills for the DeepSeek Harness · DeepSeek Harness 的长期记忆与自我演进能力 —— Plain Markdown on disk, no vector store

ProactiveAgent × DeepSeek Harness 插件组：主动记忆 + 主动建议接入 DSH（bundle 聚合包，dsh plugin add 一键安装）

Adds a shared Sup composer switch on Windows to start or gracefully stop a separate Super-agent process and release its memory. Requires a working DSH NEXT install with Node 24+, the standalone DSH CLI and an already-configured independent Super-agent profile (verified on DSH 0.2.1-rc.4/rc.5); the bundled bridge.js must also be registered in the Super-agent profile — installing the desktop package alone does not complete setup.

深鲸记忆：跨会话热记忆（每轮注入 MEMORY/USER + memory 工具读写）+ 记忆体检、瘦身归档、记忆包、诊断报告、文档只读。本地明文、零依赖、不上传数据。

Notes and todos as plain Markdown files in your workspace: type /ml plus a sentence to jot into today's journal; todos come in three flavors — deadline, sleep-until-a-date (auto-wakes when due), and anytime; /ml view fuzzy-opens any file with a few keystrokes. Zero tokens throughout — nothing goes through the model, and your notes stay ordinary local files that git and any editor can read.

PostgreSQL-backed durable memory for DSH agents: MCP memory tools plus a Web panel for reviewing memories and candidates.